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Check understanding with the teach-back method

Auteur: AILesson8 min de préparationTesté avec:ChatGPTRévisé: 2026-08-28

Réponse rapide

Test whether a learner can explain, apply, and repair a concept without copying the source. Fournir: Concept and authoritative material, Learner's teach-back, Level and feedback setting. Résultat attendu: A structured teach-back dialogue with diagnosed gaps, targeted prompts, and a retest.

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Use teach-back to check the supplied learner response against the authoritative material. Do not replace the learner's work with a polished answer immediately.

Objective, source explanation, examples, conditions, and confusions:
[concept]

Learner explanation, reasoning, example, calculation, and uncertainty:
[response]

Level, assessment boundary, allowed hints, language, tone, and time:
[setting]

Break the objective into observable components. For each, classify the response as demonstrated, partial, misconception, unsupported, or not attempted, quoting only a short relevant phrase from the learner. Distinguish wording difficulty from conceptual error and arithmetic slip from method error. Do not diagnose ability, effort, or motive. Start by naming what is correct and why.

Ask one targeted question at a time, choosing the smallest hint permitted: clarify term, request reason, contrast case, counterexample, worked step, or transfer scenario. Do not hide multiple questions in one prompt. After each imagined or supplied answer, update the diagnosis; if no answer is supplied, stop and wait rather than fabricate it. When operating as a one-shot worksheet, provide questions plus a separate answer-check rubric, not pretend dialogue. End with a parallel retest, success criteria, remaining gap, and next review action.
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De l'entrée au résultat

Un exemple détaillé

Voyez comment un contexte concret transforme cette recette en résultat utilisable.

Entrée réelle

Concept and authoritative material
Objective: explain and choose mean or median for a typical value. Source N3: mean is sum/count and is sensitive to extremes; median is ordered middle (average two middles if even) and resists a few extremes. Choose based on distribution and purpose; median can be preferable for strong skew, but an apparent extreme may be a data error that should be checked. Choosing median does not identify why data are skewed.
Learner's teach-back
The mean is the average and the median is the middle. Median is better whenever there is an outlier because the mean becomes wrong. For 2, 3, 4, 100 the median is 4, so I would report 4. I am not sure what happens with an even number of values.
Level and feedback setting
Adult beginner, formative practice, seven minutes. Ask in English, supportive and direct. Conceptual hints allowed but do not provide the final calculation before the learner attempts it. Return a one-shot worksheet because no live reply is available.

Exemple de sortie

What is demonstrated: you correctly distinguish mean as an arithmetic average and median as position-based “middle,” and you notice that 100 can pull the mean. Partial: the rule for even counts is missing. Misconception: “median is better whenever” and “mean becomes wrong” overstate the source; sensitivity does not make a correctly calculated mean wrong, and the choice depends on purpose/data quality. Method error: for four ordered values, 4 is not the median; the two middle values must be combined. Not attempted: checking whether 100 is a genuine value or error, and explaining what “typical” should mean here.

One-shot teach-back worksheet—answer before opening the rubric: Q1 only: For 2, 3, 4, 100, which two observations determine the median, and what single operation combines them? Q2: Explain why the mean can remain mathematically correct but be less useful as a typical value. Q3: Give one reason to check 100 before choosing a summary. Q4: If 100 is verified as a real high value, choose a measure for a “typical observation” and state one limitation of that choice.

Rubric: Q1 identifies 3 and 4 and averages them to 3.5. Q2 distinguishes calculation correctness from representativeness. Q3 treats data error as possible, not certain. Q4 may choose median with skew/extreme justification, while acknowledging it hides magnitude or does not explain cause; another choice is acceptable if purpose is explicit.

Parallel retest: values 5, 6, 7, 8, 54. Success requires correct mean and median method, a purpose-based choice, and one data-quality check—without “always.” Remaining gap after the original response: even-count procedure and conditional choice. Next review: redo one even-count and one verified-extreme scenario tomorrow.

Pourquoi cela fonctionne

  1. 1

    Component-level diagnosis distinguishes a missing explanation from a wrong mental model

  2. 2

    Minimal hints preserve retrieval and reasoning while still giving the learner a path to repair

Vérifier le résultat

  • Is every diagnosis tied to the learner's actual words and the supplied source

  • Does each prompt test one gap without revealing more answer than necessary

  • Does the retest use a parallel case with observable success criteria

Utilisez-la en toute confiance

Questions fréquentes

Des réponses pratiques sur le bon moment pour utiliser cette recette, ce qu’il faut fournir et les cas où une vérification humaine reste nécessaire.

What should I prepare before using “Check understanding with the teach-back method”?

For “Check understanding with the teach-back method,” prepare Concept and authoritative material, Learner's teach-back, and Level and feedback setting. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.

When is the “Check understanding with the teach-back method” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—A structured teach-back dialogue with diagnosed gaps, targeted prompts, and a retest—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.

Which AI tools have recorded tests for “Check understanding with the teach-back method”?

The published test record for “Check understanding with the teach-back method” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.

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